Aerosols are small dust particles in the air, such as soot, ash and desert dust. They have a major influence on air pollution and climate change, but their precise role is insufficiently known. That is why scenarios for global warming up to the year 2100 vary approximately 3 degrees Celsius. Most aerosols have a cooling effect by reflecting and absorbing sunlight (aerosol-radiation interactions) and by changing the properties of clouds (aerosol-cloud interactions). But one type of aerosol–soot–contributes to global warming by boosting the warming effects of greenhouse gases. At SRON we work on space instrumentation, retrieval algorithms and data exploitation to better understand and quantify the effects of aerosols on climate and air quality.
The sensitivity of cloud microphysics to aerosol loading, quantified by the aerosol-cloud interactions (ACI) index, plays a crucial role in calculating the radiative forcing due to ACI (RFaci). However, the dependence of the ACI index on liquid water content (LWC) and its impact on RFaci are often overlooked. This study aims to investigate this dependence and evaluate its implications for RFaci, based on ground in-situ aerosol-cloud observations on Mt. Lu in eastern China. The results demonstrate that the ACI index exhibits an initial increase, followed by a decline with increasing LWC. In the unconstrained LWC scenario, the ACI index, calculated based on cloud droplet number concentration (ACIn of 0.13), is found to be lower than the lower bound of ACIn (0.17 to 0.35) obtained from the constrained LWC scenario. Neglecting this LWC-dependence leads to a significant underestimation of the mean RFaci by 53%. Furthermore, when calculating RFaci using the ACI index from droplet effective diameter (ACId), implying the neglect of the dispersion effect by assuming a fixed droplet spectrum width, it results in an overestimation of RFaci by 22% compared to using ACIn. These findings shed new light on the assessment of RFaci and help reconcile differences between observed and simulated RFaci. Aerosol-cloud interactions are the major source of uncertainty in understanding human-induced climate change. This study focused on the relationship between aerosol and clouds, specifically investigating how the sensitivity of cloud properties to aerosol loading associates with the amount of liquid water present in the clouds. Using ground in-situ observations of clouds and aerosols on Mt. Lu in eastern China, this study showed that the sensitivity of cloud properties to aerosol loading initially increases and then decreases as the cloud water content increases. Neglecting this dependence can lead to a significant underestimation of the impact of aerosol-cloud interactions on climate. These findings provide valuable insights for improving our understanding of how aerosols and clouds interact and contribute to climate change.
In this study we explore aerosol-cloud interactions in liquid-phase clouds over eastern China (EC) and its adjacent ocean (ECO) using the WRF-Chem-SBM model with four-dimensional assimilation. The results show that our simulations and analyses based on each vertical layer provide a more detailed representation of the aerosol-cloud relationship compared to the column-based analyses which have been widely conducted previously. For aerosol activation, cloud droplet number concentration (Nd) generally increases with aerosol number concentration (Naero) at low Naero and decreases with Naero at high Naero. The main difference between EC and ECO is that Nd increases faster in ECO than EC at low Naero due to abundant water vapor, whereas at high Naero, when aerosol activation in ECO is suppressed, Nd in EC shows significant fluctuation due to strong surface effects (longwave radiation cooling and terrain uplift) and intense updrafts. Cloud liquid water content (CLWC) increases with Nd, but the increase rate gradually slows down for precipitating clouds, while CLWC increases and then decreases in non-precipitating clouds. Higher Nd and CLWC can be found in EC than in ECO, and the transition-point Nd value at which CLWC in non-precipitating clouds changes from increasing to decreasing is also higher in EC. Aerosol activation is strongest at moderate Naero, but CLWC increases relatively fast at low Naero. ECO cloud processes are more limited by cooling and humidification, whereas strong and diverse surface and atmospheric processes in EC allow intense cloud processes to occur under significant warming or drying conditions.
Dimethyl sulfide (DMS) is the largest source of natural sulfur in the atmosphere and undergoes oxidation reactions resulting in gas-to-particle conversion to form sulfate aerosol. Climate models typically use independent chemical schemes to simulate these processes, however, the sensitivity of sulfate aerosol to the schemes used by CMIP6 models has not been evaluated. Current climate models offer oversimplified DMS oxidation pathways, adding to the ambiguity surrounding the global sulfur burden. Here, we implemented seven DMS and sulfate chemistry schemes, six of which are from CMIP6 models, in an atmosphere-only Earth system model. A large spread in aerosol optical depth (AOD) is simulated (0.077), almost twice the magnitude of the pre-industrial to present-day increase in AOD. Differences are largely driven by the inclusion of the nighttime DMS oxidation reaction with NO3, and in the number of aqueous phase sulfate reactions. Our analysis identifies the importance of DMS-sulfate chemistry for simulating aerosols. We suggest that optimizing DMS/sulfur chemistry schemes is crucial for the accurate simulation of sulfate aerosols.
We collaborate with climate researchers and modelers, and together contribute to the development of physical instruments and the promotion of scientific activities outside SRON.
Responsible for ¼ of human-made greenhouse effect
About 30 times more powerful than CO₂ (GWP-100)
Large emissions from fossil fuel industry, landfills, livestock
Most important human-made greenhouse gas
Hard to monitor emissions because of long lifetime
Reactions with atmospheric gases contribute to global warming
Trace gas to calculate CO₂ emissions from forest fires
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